Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models.

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.

Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes.

In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. However, whereas many studies have demonstrated the changes in size that may occur during the period of pubertal growth spurt, shape changes have been less well investigated. Considering a group of subjects presenting normal occlusion, in this paper we thus describe a Bayesian functional ANOVA model that provides information about where and when the shape changes of the mandible occur during different stages of development. The model is developed by defining the notion of predictive process models for Gaussian process (GP) distributions used as priors over the random functional effects. We show that the predictive approach is computationally appealing and that it is useful to analyze multivariate functional data with unequally spaced observations that differ among subjects and times. Graphical posterior summaries show that our model is able to provide a biological interpretation of the morphometric findings and that they comprehensively describe the shape changes of the human mandible profiles. Compared with classical cephalometric analysis, this paper represents a significant methodological advance for the study of mandibular shape changes in two dimensions.

Hyperhomocysteinemia is Associated with Inflammation, Bone Resorption, Vitamin B12 and Folate Deficiency and MTHFR C677T Polymorphism in Postmenopausal Women with Decreased Bone Mineral Density.

Osteoporosis is an age-related bone disease, affecting mainly postmenopausal women, characterized by decreased bone mineral density (BMD) and consequent risk of fractures. Homocysteine (Hcy), a sulfur-aminoacid whose serum level is regulated by methylenetrahydrofolate reductase (MTHFR) activity and vitamin B12 and folate as cofactors, is a risk factor for inflammatory diseases. Literature data concerning the link between Hcy and osteoporosis are still debated. The aim of our study was to assess the relationship among Hcy and BMD, inflammation, vitamin status and bone turnover in postmenopausal osteoporosis. In 252 postmenopausal women, BMD was measured by dual-energy X-ray absorptiometry (DXA). In addition to serum Hcy, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and bone turnover markers (bone alkaline phosphatase-BAP, osteocalcin-OC, C-terminal telopeptide of type I collagen (CTX), vitamin deficiencies and MTHFR-C677T polymorphism were evaluated. Hcy, inflammation, bone resorption markers and prevalence of C677T polymorphism were higher, whereas vitamin D, B12, folate, and bone formation markers were lower in women with decreased BMD compared to those with normal BMD. Our results suggest a significant association between Hcy, BMD and inflammation in postmenopausal osteoporosis. The regulation of Hcy overproduction and the modulation of the inflammatory substrate could represent additional therapeutic approaches for osteoporosis prevention.

Aging and the Combined effects of ADRA2B and CB1 deletions on Affective Working Memory.

Many studies have found that memory for affective material is better than memory for neutral information and memory for positive material compared to negative material is better in older adults. Behavioral, neurophysiological as well as single polymorphism differences have been advanced to account for these effects. Here, we aimed to examine whether the combination of two polymorphisms (ADRA2B and CB1) in older adults influences active maintenance and manipulation of emotional information in aging working memory. We examined genotype data from 207 older adults (56 double deletion carriers, 116 single deletion carriers and 35 no deletion carriers) who performed a verbal operation span-like task with positive, negative and neutral words. We found that subjects carrying both ADRA2B and CB1 variants generally remembered a higher number of words. In addition, double carriers showed positivity effects while single carriers showed more general emotional enhancement effects, especially as strings lengthened. These findings are amongst the first to suggest a haplotype account of positivity effects in older adults' memory.

Negative Affectivity Predicts Lower Quality of Life and Metabolic Control in Type 2 Diabetes Patients: A Structural Equation Modeling Approach.

Introduction: It is essential to consider the clinical assessment of psychological aspects in patients with Diabetes Mellitus (DM), in order to prevent potentially adverse self-management care behaviors leading to diabetes-related complications, including declining levels of Quality of Life (QoL) and negative metabolic control. Purpose: In the framework of Structural Equation Modeling (SEM), the specific aim of this study is to evaluate the influence of distressed personality factors as Negative Affectivity (NA) and Social Inhibition (SI) on diabetes-related clinical variables (i.e., QoL and glycemic control). Methods: The total sample consists of a clinical sample, including 159 outpatients with Type 2 Diabetes Mellitus (T2DM), and a control group composed of 102 healthy respondents. All participants completed the following self- rating scales: The Type D Scale (DS14) and the World Health Organization QoL Scale (WHOQOLBREF). Furthermore, the participants of the clinical group were assessed for HbA1c, disease duration, and BMI. The observed covariates were BMI, gender, and disease duration, while HbA1c was considered an observed variable. Results: SEM analysis revealed significant differences between groups in regards to the latent construct of NA and the Environmental dimension of QoL. For the clinical sample, SEM showed that NA had a negative impact on both QoL dimensions and metabolic control. Conclusions: Clinical interventions aiming to improve medication adherence in patients with T2DM should include the psychological evaluation of Type D Personality traits, by focusing especially on its component of NA as a significant risk factor leading to negative health outcomes.

Quantile regression and Bayesian cluster detection to identify radon prone areas.

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.